论文标题
COVID-19流行病学作为动态传播森林的新兴行为
COVID-19 epidemiology as emergent behavior on a dynamic transmission forest
论文作者
论文摘要
在本文中，我们创建了SARS-COV-2传输的隔离，随机过程模型，该过程的平均值和方差具有独特的动态。该模型使用确定性的，生物学动机的信号处理方法适合华盛顿的时间序列数据，从2020年1月至2021年3月，我们表明该模型的隐藏状态（如人群流行）与调查和其他估计相一致。然后，在本文的下半年中，我们证明可以将同一模型重新构架为具有动态程度分布的分支过程。这种观点使我们能够生成近似的传输树，并估算一些高阶统计数据，例如将病例作为爆发的聚类，我们发现这与接触跟踪和系统发育学的相关观察结果一致。
In this paper we create a compartmental, stochastic process model of SARS-CoV-2 transmission, where the process's mean and variance have distinct dynamics. The model is fit to time series data from Washington from January 2020 to March 2021 using a deterministic, biologically-motivated signal processing approach, and we show that the model's hidden states, like population prevalence, agree with survey and other estimates. Then, in the paper's second half, we demonstrate that the same model can be reframed as a branching process with a dynamic degree distribution. This perspective allows us to generate approximate transmission trees and estimate some higher order statistics, like the clustering of cases as outbreaks, which we find to be consistent with related observations from contact tracing and phylogenetics.
